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Related Concept Videos

Introduction to Cognitive Psychology01:20

Introduction to Cognitive Psychology

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Cognitive psychology is the field of psychology dedicated to examining how people think. It attempts to explain how and why we think the way we do by studying the interactions among human thinking, emotion, creativity, language, and problem-solving, as well as other cognitive processes. Cognitive psychology studies how information is processed and manipulated in remembering, thinking, and knowing.
This field emerged in the mid-20th century, following a period dominated by behaviorism, which...
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Reason and Intuition01:37

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Cause and Effect01:53

Cause and Effect

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Inductive Reasoning00:59

Inductive Reasoning

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Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. It is uncertain and operates in degrees to which the conclusions are credible. As such, inductive arguments can be weak or strong, rather than valid or invalid, and conclusions can be used to formulate testable, falsifiable hypotheses.
Inductive reasoning is common in descriptive science. A life scientist makes observations and records them. This data can be qualitative or...
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Reasoning01:30

Reasoning

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Reasoning is the action of thinking about something in a logical, sensible way. It is integral to problem-solving, decision-making, and critical thinking. Reasoning can be inductive or deductive. Reasoning involves transforming information into conclusions, which is essential for problem-solving, decision-making, and critical thinking.
Inductive reasoning involves deriving generalizations from specific observations. This type of reasoning helps form beliefs about the world. For example,...
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Criteria for Causality: Bradford Hill Criteria - II01:28

Criteria for Causality: Bradford Hill Criteria - II

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The Bradford Hill criteria serve as guidelines for establishing causative links in epidemiological research. Beyond Strength, Consistency, Specificity, and Temporality, key criteria also include Biological Gradient, Plausibility, Coherence, Experiment, and Analogy. These principles assist scientists in assessing the likelihood of causation in complex biological contexts. Below is a summary of these concepts:
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Artificial Intelligence Is Stupid and Causal Reasoning Will Not Fix It.

J Mark Bishop1

  • 1Department of Computing, Goldsmiths, University of London, London, United Kingdom.

Frontiers in Psychology
|February 15, 2021
PubMed
Summary

Artificial intelligence (AI) excels at pattern recognition but struggles with genuine understanding. This paper argues AI errors stem not from a lack of causal reasoning, but from computation

Keywords:
Chinese room argumentPenrose-Lucas argumentartificial intelligenceartificial neural networkscausal cognitioncognitive sciencedancing with pixies

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Philosophy of Mind

Background:

  • Artificial Neural Networks (ANNs) demonstrate superhuman performance in complex games.
  • Widespread AI adoption in business leads to significant consequences when AI systems err.
  • Current AI, particularly Deep Learning, is criticized for "curve fitting" rather than causal reasoning.

Purpose of the Study:

  • To offer an alternative explanation for AI errors beyond the inability to grasp causality.
  • To re-examine AI failures through the lens of Gilbert Ryle's "category mistake" concept.
  • To propose that AI's limitations lie in its computational nature, not its reasoning capacity.

Main Methods:

  • Analysis of AI performance and failures in various domains (e.g., autonomous vehicles, chatbots).
  • Critique of prevailing explanations for AI errors, focusing on "causal reasoning" versus "association" (pattern detection).
  • Philosophical examination of computation and understanding, referencing Gilbert Ryle's work.

Main Results:

  • AI's success in pattern detection (curve fitting) does not equate to genuine understanding or causal inference.
  • The "category mistake" framework suggests AI's fundamental limitation is not a lack of causal reasoning, but its inability to "understand" anything.
  • Errors arise because AI systems, as pure computation, lack the inherent grasp of concepts like time, space, and causality.

Conclusions:

  • AI's current architecture, based on computation, prevents true comprehension, regardless of its pattern-matching prowess.
  • Shifting focus from "causal reasoning" to the fundamental nature of computation is crucial for addressing AI limitations.
  • Future AI development may require a paradigm shift beyond statistical pattern detection to achieve genuine intelligence.